Vacant lots, though overlooked or seen as eyesores by many, represent opportunities. UConn College of Agriculture, Health and Natural Resources doctoral researcher Pan Zhang and Assistant Professor Sohyun Park, both in the Department of Plant Science and Landscape Architecture, have created a framework to help cities and community members assess and prioritize which lots will have the biggest impact—for everyone—if they are repurposed. Their research is published in the journal Sustainability. Due to rapid deindustrialization and white flight, Hartford is home to some of the poorest neighborhoods in the country, and areas of North Hartford were designed as an urban renewal Promise Zone in 2008. Zhang explains the project started in 2018 as part of a class project with retired Associate Professor Kristin Schwab. The class she was taking was tasked by the planners from the City of Hartford blight remediation team along with community stakeholders to evaluate and assess city-owned vacant lots. The city wanted to have a framework to systematically manage the lots and potentially pick sites that were expected to be most suitable, and successful, for regeneration and placemaking purposes. Zhang partnered with Park to continue developing the framework after the semester. Urban greening efforts are underway in other post-industrial cities, like Detroit and Cleveland, but Park says these efforts tend to be driven by single goals, either economic or environmental. The researchers wanted to create a comprehensive framework that could accomplish many goals, and that is how they created the Vacant Land Assessment System (VLAS). These kinds of projects face several challenges, such as zoning restrictions, potential remediation of brownfield sites, ensuring the projects address community needs and avoid gentrification, as well as how to reach consensus on the reuse programs if lands were privately owned. "First, the city gave us a spreadsheet with all the street addresses of their properties and when we started to geocode the inventory, we realized there were spatial patterns that were categorizable," says Zhang. Using publicly available information and geographic information systems (ArcGIS) tools they analyzed features of the properties, geographical distribution, and potential strategies for reclaiming the vacant lots. The researchers analyzed the characteristics of the lots based on their proximity to different facilities, infrastructure, schools, and parks, for example, to assess future reuse opportunities. They organized the properties into four types, or typologies, and categorized them as Row House, Street Corner, Commercial/Industrial, and Main Street. Then reuse programs were designed for each category to create some generalized strategies. "After that, we consulted with the city about which sites to work on in North Hartford. Then we worked with the planners, neighborhood NGOs, and stakeholders to try to apply those sustainable placemaking strategies. We got good feedback and reactions from the public when we presented the final design outcomes," says Zhang. Zhang says the VLAS framework leverages existing spatial data and resources so the tools can be easily used by other planners in any municipality and can help with planning and managing spaces from site to neighborhood to city scales and can also serve as an assessment tool. Another essential quality of the framework is that it links scientific expertise with policymakers and community stakeholders to create a collaborative working environment. Though the project implementing the VLAS framework has not gone forward yet, Zhang hopes that it will one day, "I continued to work in that neighborhood the summer after that project and residents still remember me and that project. It is something the residents were looking forward to." Zhang feels the approach could have lasting ecological impacts as more greening lots could not only increase access to green spaces but also increase connectivity with forests in and around cities. "We want to greenify those lands that have been disregarded and underestimated in the city setting. The existing native trees in those vacant lots might have more potential than people think," says Park. "They might be good for local ecosystems, even though that's not an intact ecosystem, but rather what's called a novel ecosystem where urban wildlife can thrive. Also, actively greenifying those lands helps the community's health and well-being in the long term and may be able to help break the cycle of poverty, and violence that is prevalent in those areas. "Even though this is a small case study, when we can scale up these practices to a broader level, we might be touching upon some societal problems that we have. There might be some implications that we can draw from this research." By using the holistic approach and multi-scale thinking of VLSA, greening vacant lots could be for the common benefit of all. Park stresses that community engagement is key. "Even though this is a research-based, data-driven study, all things that could happen should be involved with members who live in that neighborhood. I think connections from the research to community engagement and participation should be key to making things happen." source: VLAS: Vacant Land Assessment System for Urban Renewal and Greenspace Planning in Legacy Cities

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A new study introduces the Community Land Active Passive Microwave Radiative Transfer Modeling platform (CLAP)—a unified multi-frequency microwave scattering and emission model designed to revolutionize land surface monitoring. This cutting-edge platform combines active and passive microwave signals to offer potentially accurate simulations of soil moisture and vegetation conditions. By incorporating advanced interaction models for soil and vegetation, CLAP has the potential to address key limitations in existing remote sensing technologies, enabling the improvement of land monitoring precision. The study showcases CLAP's ability to improve microwave signal simulations, especially at high frequencies, marking a major step forward in ecosystem management and climate change research. Microwave remote sensing is essential for land monitoring, providing crucial insights into soil moisture and vegetation health by measuring the microwave radiation and backscatter emitted and scattered by the surface. However, current models rely heavily on zeroth-order radiative transfer theory and empirical assumptions, often overlooking dynamic changes in vegetation and soil properties (structure, moisture and temperature). These limitations result in inconsistencies and reduced accuracy across different frequencies and polarizations. Given these challenges, there is an urgent need for more refined research into the scattering and emission mechanisms of multi-frequency microwave signals to improve the precision and reliability of remote sensing technologies. A team of researchers from the University of Twente has published a paper in the Journal of Remote Sensing, introducing the Community Land Active Passive Microwave Radiative Transfer Modeling platform (CLAP) a multi-frequency microwave scattering and emission model, which integrates advanced soil surface scattering (ATS+AIEM) and vegetation scattering (TVG) models. CLAP incorporates appropriate vegetation structure, dynamic vegetation water content (VWC) and temperature changes, significantly improving upon existing technologies. Additionally, CLAP uncovers the frequency-dependent nature of grassland optical depth and highlights the significant impact of vegetation temperature on high-frequency signals, offering new insights for more accurate vegetation and soil monitoring. The core strength of CLAP lies in its detailed modeling of soil and vegetation components. The team used long-term in situ observations from the Maqu site, including microwave signals, soil moisture, temperature profiles, and vegetation data, to drive CLAP and evaluate the model performance respectively. Results showed that during the summer, CLAP with cylinder parameterization for vegetation representation simulated grassland backscatter at X-band and C-band with RMSE values of 1.8 dB and 1.9 dB, respectively, compared to 3.4 dB and 3.0 dB from disk parameterization. The study also discovered that vegetation temperature variations significantly affect high-frequency signal diurnal changes, while vegetation water content changes primarily influence low-frequency signals. For example, at C-band, vegetation temperature fluctuations had a greater impact on signal changes (correlation coefficient R of 0.34), while at S-band, vegetation water content had a stronger influence (R of 0.46). These findings underscore the importance of dynamic vegetation and soil properties in microwave signal scattering and emission processes, which CLAP accurately reflects. Dr. Hong Zhao, the lead researcher, commented, "The CLAP platform represents a major advancement in microwave remote sensing. By incorporating appropriate vegetation structure, dynamic vegetation and soil water content and temperature into the model, CLAP offers a more accurate representation of microwave signal scattering and emission processes. This innovation will significantly enhance our ability to monitor vegetation and soil conditions, providing more reliable data for ecosystem management and climate change research." The team utilized extensive in situ data from the Maqu site as well as satellite microwave observations. These comprehensive datasets allowed the researchers to rigorously assess CLAP's performance across various frequencies and polarizations, ensuring its accuracy and reliability. The development of CLAP opens new possibilities for the future of microwave remote sensing. This technology can be integrated into upcoming satellite missions such as CIMR and ROSE-L to enhance the precision of soil moisture and vegetation monitoring. Additionally, CLAP can be incorporated into data assimilation frameworks to provide more accurate inputs for land surface models. The widespread application of this technology promises to have a profound impact on global environmental monitoring, agricultural production, and climate change research, supporting sustainable development efforts worldwide. source: https://dx.doi.org/10.34133/remotesensing.0415  

Researchers at Aalto University have, for the first time, investigated the occurrence of wolverines across the whole of Finland using satellite imagery, field measurements, and snow track observations. The wolverine, a predator typically found in the fells and forests of northern Finland, was classified as endangered in the country already in the 1980s. Although information on the species' historical range is limited, wolverines are known to have inhabited southern Finland as recently as the 19th century. Hunting caused the species to disappear from the region. This study, published in the journal Ecology and Evolution, is the first to provide nationwide data on the types of habitats favored by wolverines as they expand into new areas. "The species is returning to its historical range in southern Finland. According to our research, the deciduous-dominated mixed forests typical of the south may be more important habitats for wolverines than previously thought," says Pinja-Emilia Lämsä, a doctoral researcher at Aalto University. Despite recent population growth, the wolverine's survival remains threatened by its small population size, low genetic viability, and fragmented distribution. However, the study's use of remote sensing and field data offers vital information for safeguarding biodiversity. "Understanding habitats is essential for improving species conservation and management," says Professor Miina Rautiainen, a remote sensing expert at Aalto University. Fragmentation of forest landscapes poses a threat The study found that wolverines tend to favor large, forested areas with deciduous trees. They were rarely observed near recent clear-cuts, whereas older felling sites—about 10 years old—were more attractive. Wolverines also preferred areas with less dense tree cover. Previous studies on wolverine habitats and distribution have mainly focused on mountainous regions with vegetation that differs significantly from the low-lying boreal forests of Finland. According to Pinja-Emilia Lämsä, it is crucial to understand which habitats wolverines prefer specifically in Finland, where forestry practices strongly influence forest structure. "In Finland, the average forest compartment—a uniform section of forest in terms of tree species and site conditions—is relatively small. This can lead to a patchwork-like fragmentation of forest landscapes in forest management decisions. To protect wolverine habitats, mixed-species forest should be prioritized and large, continuous forest areas preserved," Lämsä says. Remote sensing reveals impacts of environmental change The study, conducted in collaboration with the Natural Resources Institute Finland (Luke), combined snow track counts of wolverines with national forest inventory data based on satellite images and field measurements. This approach allowed the researchers to examine the influence of forest characteristics on wolverine presence on a large scale. According to Rautiainen, remote sensing is an excellent tool for studying the distribution of animal species across broad areas, as satellite and aerial images provide increasingly detailed information about changes in forest landscapes and their impacts on wildlife. "In the future, remote sensing will enable us to monitor in even greater detail how, for example, changes in vegetation or other environmental factors in Finland affect animal populations," Rautiainen says. source: https://dx.doi.org/10.1002/ece3.71300